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NEW QUESTION # 54
Which of the following code blocks will remove the rows where the value in column age is greater than 25 from the existing Delta table my_table and save the updated table?
- A. UPDATE my_table WHERE age <= 25;
- B. DELETE FROM my_table WHERE age > 25;
- C. SELECT * FROM my_table WHERE age > 25;
- D. UPDATE my_table WHERE age > 25;
- E. DELETE FROM my_table WHERE age <= 25;
Answer: B
Explanation:
The DELETE command in Delta Lake allows you to remove data that matches a predicate from a Delta table.
This command will delete all the rows where the value in the column age is greater than 25 from the existing Delta table my_table and save the updated table. The other options are either incorrect or do not achieve the desired result. Option A will only select the rows that match the predicate, but not delete them. Option B will update the rows that match the predicate, but not delete them. Option D will update the rows that do not match the predicate, but not delete them. Option E will delete the rows that do not match the predicate, which is the opposite of what we want. References: Table deletes, updates, and merges - Delta Lake Documentation
NEW QUESTION # 55
A data engineer has been using a Databricks SQL dashboard to monitor the cleanliness of the input data to an ELT job. The ELT job has its Databricks SQL query that returns the number of input records containing unexpected NULL values. The data engineer wants their entire team to be notified via a messaging webhook whenever this value reaches 100.
Which of the following approaches can the data engineer use to notify their entire team via a messaging webhook whenever the number of NULL values reaches 100?
- A. They can set up an Alert without notifications.
- B. They can set up an Alert with one-time notifications.
- C. They can set up an Alert with a new email alert destination.
- D. They can set up an Alert with a custom template.
- E. They can set up an Alert with a new webhook alert destination.
Answer: E
Explanation:
A webhook alert destination is a way to send notifications to external applications or services via HTTP requests. A data engineer can use a webhook alert destination to notify their entire team via a messaging webhook, such as Slack or Microsoft Teams, whenever the number of NULL values in the input data reaches 100. To set up a webhook alert destination, the data engineer needs to do the following steps:
In the Databricks SQL workspace, navigate to the Settings gear icon and select SQL Admin Console.
Click Alert Destinations and click Add New Alert Destination.
Select Webhook and enter the webhook URL and the optional custom template for the notification message.
Click Create to save the webhook alert destination.
In the Databricks SQL editor, create or open the query that returns the number of input records containing unexpected NULL values.
Click the Create Alert icon above the editor window and configure the alert criteria, such as the value column, the condition, and the threshold.
In the Notification section, select the webhook alert destination that was created earlier and click Create Alert. Reference: What are Databricks SQL alerts?, Monitor alerts, Monitoring Your Business with Alerts, Using Automation Runbook Webhooks To Alert on Databricks Status Updates.
NEW QUESTION # 56
Which of the following commands will return the location of database customer360?
- A. DROP DATABASE customer360;
- B. DESCRIBE DATABASE customer360;
- C. USE DATABASE customer360;
- D. ALTER DATABASE customer360 SET DBPROPERTIES ('location' = '/user'};
- E. DESCRIBE LOCATION customer360;
Answer: B
Explanation:
The command DESCRIBE DATABASE customer360; will return the location of the database customer360, along with its comment and properties. This command is an alias for DESCRIBE SCHEMA customer360;, which can also be used to get the same information. The other commands will either drop the database, alter its properties, or use it as the current database, but will not return its location12. Reference:
DESCRIBE DATABASE | Databricks on AWS
DESCRIBE DATABASE - Azure Databricks - Databricks SQL
NEW QUESTION # 57
A data engineer has a Job with multiple tasks that runs nightly. Each of the tasks runs slowly because the clusters take a long time to start.
Which of the following actions can the data engineer perform to improve the start up time for the clusters used for the Job?
- A. They can use endpoints available in Databricks SQL
- B. They can use clusters that are from a cluster pool
- C. They can configure the clusters to autoscale for larger data sizes
- D. They can use jobs clusters instead of all-purpose clusters
- E. They can configure the clusters to be single-node
Answer: B
Explanation:
The best action that the data engineer can perform to improve the start up time for the clusters used for the Job is to use clusters that are from a cluster pool. A cluster pool is a set of idle clusters that can be used by jobs or interactive sessions. By using a cluster pool, the data engineer can avoid the cluster creation time and reduce the latency of the tasks. Cluster pools also offer cost savings and resource efficiency, as they can be shared by multiple users and jobs.
Option A is not relevant, as endpoints available in Databricks SQL are used for creating and managing SQL analytics workloads, not for improving cluster start up time.
Option B is not correct, as jobs clusters and all-purpose clusters have similar start up times. Jobs clusters are clusters that are dedicated to run a single job and are terminated when the job is completed. All-purpose clusters are clusters that can be used for multiple purposes, such as interactive sessions, notebooks, or multiple jobs. Both types of clusters can benefit from using a cluster pool.
Option C is not advisable, as configuring the clusters to be single-node will reduce the parallelism and performance of the tasks. Single-node clusters are clusters that have only one worker node and are typically used for testing or development purposes. They are not suitable for running production jobs that require high scalability and fault tolerance.
Option E is not helpful, as configuring the clusters to autoscale for larger data sizes will not affect the start up time of the clusters. Autoscaling is a feature that allows clusters to dynamically adjust the number of worker nodes based on the workload. It can help optimize the resource utilization and cost efficiency of the clusters, but it does not speed up the cluster creation process.
References:
* Cluster Pools
* Jobs
* Clusters
* [Databricks Data Engineer Professional Exam Guide]
NEW QUESTION # 58
Which of the following describes a benefit of creating an external table from Parquet rather than CSV when using a CREATE TABLE AS SELECT statement?
- A. Parquet files will become Delta tables
- B. Parquet files have a well-defined schema
- C. Parquet files can be partitioned
- D. CREATE TABLE AS SELECT statements cannot be used on files
- E. Parquet files have the ability to be optimized
Answer: B
Explanation:
Explanation
https://www.databricks.com/glossary/what-is-parquet#:~:text=Columnar%20storage%20like%20Apache%20Par Columnar storage like Apache Parquet is designed to bring efficiency compared to row-based files like CSV.
When querying, columnar storage you can skip over the non-relevant data very quickly. As a result, aggregation queries are less time-consuming compared to row-oriented databases.
NEW QUESTION # 59
A data engineer has developed a data pipeline to ingest data from a JSON source using Auto Loader, but the engineer has not provided any type inference or schema hints in their pipeline. Upon reviewing the data, the data engineer has noticed that all of the columns in the target table are of the string type despite some of the fields only including float or boolean values.
Which of the following describes why Auto Loader inferred all of the columns to be of the string type?
- A. Auto Loader only works with string data
- B. JSON data is a text-based format
- C. All of the fields had at least one null value
- D. Auto Loader cannot infer the schema of ingested data
- E. There was a type mismatch between the specific schema and the inferred schema
Answer: B
Explanation:
JSON data is a text-based format that represents data as a collection of name-value pairs. By default, when Auto Loader infers the schema of JSON data, it treats all columns as strings. This is because JSON data can have varying data types for the same column across different files or records, and Auto Loader does not attempt to reconcile these differences. For example, a column named "age" may have integer values in some files, but string values in others. To avoid data loss or errors, Auto Loader infers the column as a string type. However, Auto Loader also provides an option to infer more precise column types based on the sample data. This option is called cloudFiles.inferColumnTypes and it can be set to true or false. When set to true, Auto Loader tries to infer the exact data types of the columns, such as integers, floats, booleans, or nested structures. When set to false, Auto Loader infers all columns as strings. The default value of this option is false. Reference: Configure schema inference and evolution in Auto Loader, Schema inference with auto loader (non-DLT and DLT), Using and Abusing Auto Loader's Inferred Schema, Explicit path to data or a defined schema required for Auto loader.
NEW QUESTION # 60
A new data engineering team has been assigned to work on a project. The team will need access to database customers in order to see what tables already exist. The team has its own group team.
Which of the following commands can be used to grant the necessary permission on the entire database to the new team?
- A. GRANT USAGE ON DATABASE customers TO team;
- B. GRANT USAGE ON CATALOG team TO customers;
- C. GRANT CREATE ON DATABASE team TO customers;
- D. GRANT CREATE ON DATABASE customers TO team;
- E. GRANT VIEW ON CATALOG customers TO team;
Answer: A
Explanation:
Explanation
The GRANT statement is used to grant privileges on a database, table, or view to a user or role. The ALL PRIVILEGES option grants all possible privileges on the specified object, such as CREATE, SELECT, MODIFY, and USAGE. The syntax of the GRANT statement is:
GRANT privilege_type ON object TO user_or_role;
Therefore, to grant full permissions on the database customers to the new data engineering team, the command should be:
GRANT ALL PRIVILEGES ON DATABASE customers TO team;
NEW QUESTION # 61
A data engineer is using the following code block as part of a batch ingestion pipeline to read from a composable table:
Which of the following changes needs to be made so this code block will work when the transactions table is a stream source?
- A. Replace schema(schema) with option ("maxFilesPerTrigger", 1)
- B. Replace format("delta") with format("stream")
- C. Replace "transactions" with the path to the location of the Delta table
- D. Replace predict with a stream-friendly prediction function
- E. Replace spark.read with spark.readStream
Answer: E
Explanation:
1: To read from a stream source, the data engineer needs to use the spark.readStream method instead of the spark.read method. The spark.readStream method returns a DataStreamReader object that can be used to specify the details of the input source, such as the format, the schema, the path, and the options. The spark.read method is only suitable for batch processing, not streaming processing. The other changes are not necessary or correct for reading from a stream source. Reference: Structured Streaming Programming Guide, Read a stream, Databricks Data Sources
NEW QUESTION # 62
A data engineer needs to create a table in Databricks using data from a CSV file at location /path/to/csv.
They run the following command:
Which of the following lines of code fills in the above blank to successfully complete the task?
- A. USING DELTA
- B. FROM CSV
- C. None of these lines of code are needed to successfully complete the task
- D. USING CSV
- E. FROM "path/to/csv"
Answer: D
NEW QUESTION # 63
A dataset has been defined using Delta Live Tables and includes an expectations clause:
CONSTRAINT valid_timestamp EXPECT (timestamp > '2020-01-01') ON VIOLATION DROP ROW What is the expected behavior when a batch of data containing data that violates these constraints is processed?
- A. Records that violate the expectation are dropped from the target dataset and recorded as invalid in the event log.
- B. Records that violate the expectation cause the job to fail.
- C. Records that violate the expectation are dropped from the target dataset and loaded into a quarantine table.
- D. Records that violate the expectation are added to the target dataset and recorded as invalid in the event log.
- E. Records that violate the expectation are added to the target dataset and flagged as invalid in a field added to the target dataset.
Answer: A
Explanation:
Explanation
With the defined constraint and expectation clause, when a batch of data is processed, any records that violate the expectation (in this case, where the timestamp is not greater than '2020-01-01') will be dropped from the target dataset. These dropped records will also be recorded as invalid in the event log, allowing for auditing and tracking of the data quality issues without causing the entire job to fail.
https://docs.databricks.com/en/delta-live-tables/expectations.html
NEW QUESTION # 64
Which of the following Structured Streaming queries is performing a hop from a Silver table to a Gold table?
- A.
- B.
- C.
- D.
- E.
Answer: B
NEW QUESTION # 65
A data engineer has been given a new record of data:
id STRING = 'a1'
rank INTEGER = 6
rating FLOAT = 9.4
Which of the following SQL commands can be used to append the new record to an existing Delta table my_table?
- A. INSERT INTO my_table VALUES ('a1', 6, 9.4)
- B. UPDATE VALUES ('a1', 6, 9.4) my_table
- C. my_table UNION VALUES ('a1', 6, 9.4)
- D. INSERT VALUES ( 'a1' , 6, 9.4) INTO my_table
- E. UPDATE my_table VALUES ('a1', 6, 9.4)
Answer: A
NEW QUESTION # 66
Which of the following commands will return the location of database customer360?
- A. DROP DATABASE customer360;
- B. DESCRIBE DATABASE customer360;
- C. USE DATABASE customer360;
- D. ALTER DATABASE customer360 SET DBPROPERTIES ('location' = '/user'};
- E. DESCRIBE LOCATION customer360;
Answer: B
Explanation:
The command DESCRIBE DATABASE customer360; will return the location of the database customer360, along with its comment and properties. This command is an alias for DESCRIBE SCHEMA customer360;, which can also be used to get the same information. The other commands will either drop the database, alter its properties, or use it as the current database, but will not return its location12. References:
* DESCRIBE DATABASE | Databricks on AWS
* DESCRIBE DATABASE - Azure Databricks - Databricks SQL
NEW QUESTION # 67
Which of the following approaches should be used to send the Databricks Job owner an email in the case that the Job fails?
- A. There is no way to notify the Job owner in the case of Job failure
- B. Setting up an Alert in the Notebook
- C. Setting up an Alert in the Job page
- D. MLflow Model Registry Webhooks
- E. Manually programming in an alert system in each cell of the Notebook
Answer: C
Explanation:
To send the Databricks Job owner an email in the case that the Job fails, the best approach is to set up an Alert in the Job page. This way, the Job owner can configure the email address and the notification type for the Job failure event. The other options are either not feasible, not reliable, or not relevant for this task. Manually programming an alert system in each cell of the Notebook is tedious and error-prone. Setting up an Alert in the Notebook is not possible, as Alerts are only available for Jobs and Clusters. There is a way to notify the Job owner in the case of Job failure, so option D is incorrect. MLflow Model Registry Webhooks are used for model lifecycle events, not Job events, so option E is not applicable. Reference:
Add email and system notifications for job events
Alerts
MLflow Model Registry Webhooks
NEW QUESTION # 68
A data engineer needs to apply custom logic to string column city in table stores for a specific use case. In order to apply this custom logic at scale, the data engineer wants to create a SQL user-defined function (UDF).
Which of the following code blocks creates this SQL UDF?
- A.
- B.
- C.
- D.
- E.
Answer: B
NEW QUESTION # 69
A data analyst has created a Delta table sales that is used by the entire data analysis team. They want help from the data engineering team to implement a series of tests to ensure the data is clean. However, the data engineering team uses Python for its tests rather than SQL.
Which of the following commands could the data engineering team use to access sales in PySpark?
- A. spark.sql("sales")
- B. spark.table("sales")
- C. There is no way to share data between PySpark and SQL.
- D. SELECT * FROM sales
- E. spark.delta.table("sales")
Answer: B
NEW QUESTION # 70
A data organization leader is upset about the data analysis team's reports being different from the data engineering team's reports. The leader believes the siloed nature of their organization's data engineering and data analysis architectures is to blame.
Which of the following describes how a data lakehouse could alleviate this issue?
- A. Both teams would use the same source of truth for their work
- B. Both teams would be able to collaborate on projects in real-time
- C. Both teams would reorganize to report to the same department
- D. Both teams would autoscale their work as data size evolves
- E. Both teams would respond more quickly to ad-hoc requests
Answer: A
Explanation:
A data lakehouse is a data management architecture that combines the flexibility, cost-efficiency, and scale of data lakes with the data management and ACID transactions of data warehouses, enabling business intelligence (BI) and machine learning (ML) on all data12. By using a data lakehouse, both the data analysis and data engineering teams can access the same data sources and formats, ensuring data consistency and quality across their reports. A data lakehouse also supports schema enforcement and evolution, data validation, and time travel to old table versions, which can help resolve data conflicts and errors1. Reference: 1: What is a Data Lakehouse? - Databricks 2: What is a data lakehouse? | IBM
NEW QUESTION # 71
Which of the following commands will return the number of null values in the member_id column?
- A. SELECT count_null(member_id) FROM my_table;
- B. SELECT null(member_id) FROM my_table;
- C. SELECT count_if(member_id IS NULL) FROM my_table;
- D. SELECT count(member_id) - count_null(member_id) FROM my_table;
- E. SELECT count(member_id) FROM my_table;
Answer: C
Explanation:
To return the number of null values in the member_id column, the best option is to use the count_if function, which counts the number of rows that satisfy a given condition. In this case, the condition is that the member_id column is null. The other options are either incorrect or not supported by Spark SQL. Option A will return the number of non-null values in the member_id column. Option B will not work because there is no count_null function in Spark SQL. Option D will not work because there is no null function in Spark SQL.
Option E will not work because there is no count_null function in Spark SQL. References:
* Built-in Functions - Spark SQL, Built-in Functions
* count_if - Spark SQL, Built-in Functions
NEW QUESTION # 72
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Preparing for the Databricks-Certified-Data-Engineer-Associate exam requires a deep understanding of Databricks and its various components. Candidates should have experience with Spark SQL, Spark Streaming, and Spark MLlib, as well as a solid understanding of data modeling, data structures, and algorithms. They should also be familiar with common data storage and processing technologies, such as Hadoop, Kafka, and AWS S3.
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